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Deep Learning Code Generation from Simulink Applications

Generate C/C++ and GPU code for deployment on desktop or embedded targets

Generate code for pretrained deep neural networks. You can accelerate the simulation of your algorithms in Simulink® by using different execution environments. By using support packages, you can also generate and deploy C/C++ and CUDA® code on target hardware.

Topics

GPU Code Generation for Deep Learning Networks Using MATLAB Function Block (GPU Coder)

Simulate and generate code for deep learning models in Simulink using MATLAB function blocks.

GPU Code Generation for Blocks from the Deep Neural Networks Library (GPU Coder)

Simulate and generate code for deep learning models in Simulink using library blocks.

Code Generation for a Deep Learning Simulink Model that Performs Lane and Vehicle Detection (GPU Coder)

This example shows how to develop a CUDA® application from a Simulink® model that performs lane and vehicle detection using convolutional neural networks (CNN).

Generate Generic C/C++ for Sequence-to-Sequence Deep Learning Simulink Models (Simulink Coder)

Generate C/C++ code for a sequence-to-sequence deep learning Simulink model.